Only single-component vaccines should be used for the birth dose and doses administered before age 6 weeks. (Source: CDC)
Combined hepatitis A and B vaccine is licensed for children from 1 year old. (Source: VNVC)
4 in 1, 5 in 1, and 6 in 1 vaccines are licensed for children aged 6 weeks through 6 years. (Source: CDC)
According to the Vietnamese vaccination schedule, 4 in 1, 5 in 1, and 6 in 1 vaccines should be administered in months 2, 3, 4. (Source: Tiem Chung Mo Rong)
library(tidyverse)
library(readxl)
library(writexl)
library(janitor)
library(Hmisc)
library(arrow)
library(gtsummary)
library(plotly)
library(lubridate)
library(data.table)
library(summarytools)
library(here)# Import and review data
data <- read_parquet(here::here("data/hepb_data_long.parquet")) %>% collect()## Warning: Potentially unsafe or invalid elements have been discarded from R metadata.
## ℹ Type: "externalptr"
## → If you trust the source, you can set `options(arrow.unsafe_metadata = TRUE)` to preserve them.print(data)## pid vacname vacdate vactype
## <char> <char> <Date> <char>
## 1: 221194720170013 Quinvaxem 2017-07-20 TCCD
## 2: 221031520170032 Quinvaxem 2017-07-20 TCCD
## 3: 221031520170019 Quinvaxem 2017-07-20 TCCD
## 4: 221031520170025 Quinvaxem 2017-07-20 TCCD
## 5: 221131920170027 Quinvaxem 2017-07-19 TCCD
## ---
## 40421967: 217182920220004 SII 2022-05-17 TCMR
## 40421968: 217202720220001 SII 2022-05-18 TCMR
## 40421969: 217011420220019 Hep B vaccine for newborn 2022-05-19 TCMR
## 40421970: 217200520220010 SII 2022-05-17 TCMR
## 40421971: 217151520210070 SII 2022-05-16 TCMR
## vacplace vacplace_type province_reg
## <char> <char> <char>
## 1: TYT Đồng Việt TCMR Bắc Giang
## 2: TYT Tam Tiến TCMR Bắc Giang
## 3: TYT Tam Tiến TCMR Bắc Giang
## 4: TYT Tam Tiến TCMR Bắc Giang
## 5: TYT Vĩnh Khương-đã sát nhập TCMR Bắc Giang
## ---
## 40421967: TYT Tu Vũ TCMR Phú Thọ
## 40421968: TYT Xuân Sơn TCMR Phú Thọ
## 40421969: Bệnh viện Sản Nhi tỉnh Phú Thọ BV Phú Thọ
## 40421970: TYT Thu Cúc TCMR Phú Thọ
## 40421971: TYT Xuân Thủy TCMR Phú Thọ
## district_reg commune_reg sex dob ethnic
## <char> <char> <char> <Date> <char>
## 1: Yên Dũng Đồng Việt nam 2017-02-03 Kinh
## 2: Yên Thế Tam Tiến nu 2017-04-21 Kinh
## 3: Yên Thế Tam Tiến nam 2017-03-09 Kinh
## 4: Yên Thế Tam Tiến nam 2017-03-30 Kinh
## 5: Sơn Động Vĩnh An nam 2017-04-14 Kinh
## ---
## 40421967: Thanh Thủy Tu Vũ nam 2022-01-12 Kinh
## 40421968: Tân Sơn Xuân Sơn nu 2022-01-13 Mường
## 40421969: Thành phố Việt Trì Bến Gót nam 2022-05-19 Kinh
## 40421970: Tân Sơn Thu Cúc nam 2022-01-20 Mường
## 40421971: Yên Lập Xuân Thủy nu 2021-11-24 Kinhstr(data)## Classes 'data.table' and 'data.frame': 40421971 obs. of 12 variables:
## $ pid : chr "221194720170013" "221031520170032" "221031520170019" "221031520170025" ...
## $ vacname : chr "Quinvaxem" "Quinvaxem" "Quinvaxem" "Quinvaxem" ...
## $ vacdate : Date, format: "2017-07-20" "2017-07-20" ...
## $ vactype : chr "TCCD" "TCCD" "TCCD" "TCCD" ...
## $ vacplace : chr "TYT Đồng Việt" "TYT Tam Tiến" "TYT Tam Tiến" "TYT Tam Tiến" ...
## $ vacplace_type: chr "TCMR" "TCMR" "TCMR" "TCMR" ...
## $ province_reg : chr "Bắc Giang" "Bắc Giang" "Bắc Giang" "Bắc Giang" ...
## $ district_reg : chr "Yên Dũng" "Yên Thế" "Yên Thế" "Yên Thế" ...
## $ commune_reg : chr "Đồng Việt" "Tam Tiến" "Tam Tiến" "Tam Tiến" ...
## $ sex : chr "nam" "nu" "nam" "nam" ...
## $ dob : Date, format: "2017-02-03" "2017-04-21" ...
## $ ethnic : chr "Kinh" "Kinh" "Kinh" "Kinh" ...# Remove Boostrix, not HepB vaccine
data <- data %>% filter(vacname != "Boostrix")
# Check the number of entries not within the data collection time interval
data %>% filter(year(vacdate) < 2014 | year(vacdate) > 2022) %>% nrow()## [1] 622# Select only injection correct entries from 2014 to 2022
data <- data %>% filter(year(vacdate) >= 2014 & year(vacdate) <= 2022)#check missing values
missing_values <- is.na(data)
missing_counts <- colSums(is.na(data))
rows_with_missing <- data[!complete.cases(data), ]
print(rows_with_missing)## pid vacname vacdate vactype
## <char> <char> <Date> <char>
## 1: 221192720170048 Quinvaxem 2017-07-20 TCCD
## 2: 221011820190065 Hexaxim 2019-06-23 TCMR
## 3: 221153120190139 Hep B vaccine for newborn 2019-06-05 TCCD
## 4: 103051120180363 Hexaxim 2019-06-27 TCMR
## 5: 805011920210311 Hep B vaccine for newborn 2021-07-05 TCMR
## ---
## 1735181: 217110120220004 Infanrix Hexa 2022-03-19 TCMR
## 1735182: 217134320210195 SII 2022-03-16 TCCD
## 1735183: 217113720210100 Hexaxim 2021-12-15 TCDV
## 1735184: 217181720210236 Infanrix Hexa 2022-02-15 TCMR
## 1735185: 217010720210145 Hexaxim 2021-11-30 TCMR
## vacplace vacplace_type province_reg
## <char> <char> <char>
## 1: <NA> <NA> Bắc Giang
## 2: Trung tâm tiêm chủng VNVC Skyline Văn Quán DV Bắc Giang
## 3: <NA> <NA> Bắc Giang
## 4: Trung tâm tiêm chủng VNVC 180 Trường Chinh DV Bắc Giang
## 5: <NA> <NA> An Giang
## ---
## 1735181: <NA> <NA> Phú Thọ
## 1735182: <NA> <NA> Phú Thọ
## 1735183: <NA> Dia diem khac Phú Thọ
## 1735184: <NA> <NA> Phú Thọ
## 1735185: <NA> <NA> Phú Thọ
## district_reg commune_reg sex dob ethnic
## <char> <char> <char> <Date> <char>
## 1: Yên Dũng Tiền Phong nu 2017-03-18 Kinh
## 2: <NA> Dĩnh Trì nam 2019-01-07 Kinh
## 3: Lục Nam Nghĩa Phương nam 2019-06-04 Kinh
## 4: <NA> Trần Nguyên Hãn nu 2018-11-03 Kinh
## 5: Thành phố Long Xuyên Mỹ Hòa Hưng nu 2021-07-05 Kinh
## ---
## 1735181: Phù Ninh Thị trấn Phong Châu nu 2022-01-04 Kinh
## 1735182: Cẩm Khê Tạ Xá nam 2021-12-12 Kinh
## 1735183: Phù Ninh Bình Phú nam 2021-08-23 Kinh
## 1735184: Thanh Thủy Sơn Thủy nam 2021-10-05 Kinh
## 1735185: Thành phố Việt Trì Gia Cẩm nu 2021-07-04 Kinhyear_dob and year_vac are the years in which the child was born and vaccinated, respectively.
# Mutate columns for year of birth and year of vaccination
data <- data %>% mutate(year_dob = year(dob), year_vac = year(vacdate))Representing the age in days at which the child received the vaccine.
data <- data %>% mutate(vac_age = difftime(vacdate, dob, units = "days"))Representing the month after birth that the child receives the vaccine.
data <- data %>% mutate(vac_month = case_when(
vac_age < 0 ~ NA,
vac_age <= 1 ~ -2,
vac_age <= 7 & vac_age > 1 ~ -1,
vac_age < 30 & vac_age > 7 ~ 0,
vac_age >= 30 & vac_age < 60 ~ 1,
vac_age >= 60 & vac_age < 90 ~ 2,
vac_age >= 90 & vac_age < 120 ~ 3,
vac_age >= 120 & vac_age < 150 ~ 4,
vac_age >= 150 & vac_age < 180 ~ 5,
vac_age >= 180 & vac_age < 210 ~ 6,
vac_age >= 210 & vac_age < 240 ~ 7,
vac_age >= 240 & vac_age < 270 ~ 8,
vac_age >= 270 & vac_age < 300 ~ 9,
vac_age >= 300 & vac_age < 330 ~ 10,
vac_age >= 330 ~ 11
))Vietnam is officially divided into six regions (source1,source2, source3.)
data <- data %>%
mutate(region = case_when(
province_reg %in% c("Hà Nội", "Vĩnh Phúc", "Bắc Ninh", "Quảng Ninh", "Hải Dương", "Hải Phòng", "Hưng Yên", "Thái Bình", "Hà Nam", "Nam Định", "Ninh Bình") ~ "RRD",
province_reg %in% c("Hà Giang", "Cao Bằng", "Bắc Kạn", "Tuyên Quang", "Lào Cai", "Yên Bái", "Thái Nguyên", "Lạng Sơn", "Bắc Giang", "Phú Thọ", "Điện Biên", "Lai Châu", "Sơn La", "Hòa Bình") ~ "NMM",
province_reg %in% c("Thanh Hóa", "Nghệ An", "Hà Tĩnh", "Quảng Bình", "Quảng Trị", "Thừa Thiên Huế", "Đà Nẵng", "Quảng Nam", "Quảng Ngãi", "Bình Định", "Phú Yên", "Khánh Hòa", "Ninh Thuận", "Bình Thuận") ~ "NCC",
province_reg %in% c("Kon Tum", "Gia Lai", "Đắk Lắk", "Đắk Nông", "Lâm Đồng") ~ "CHL",
province_reg %in% c("Bình Phước", "Tây Ninh", "Bình Dương", "Đồng Nai", "Bà Rịa - Vũng Tàu", "Thành phố Hồ Chí Minh") ~ "SE",
province_reg %in% c("Long An", "Tiền Giang", "Bến Tre", "Trà Vinh", "Vĩnh Long", "Đồng Tháp", "An Giang", "Kiên Giang", "Cần Thơ", "Hậu Giang", "Sóc Trăng", "Bạc Liêu", "Cà Mau") ~ "MKD",
.default = NULL
)) %>%
mutate(region = factor(region, levels = c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")))# Classify vacname and create a new variable vacgroup
data <- data %>%
mutate(vacgroup = case_when(
vacname %in% c("ENGERIX-B", "Euvax B", "Gene-Hbvax", "H-B-VAX II", "HBVaxPRO", "r-HBvax", "Heberbiovac HB", "Hep B vaccine", "Hep B vaccine for newborn", "SCI-B-VAC", "Hepavax-Gene TF") ~ "single",
vacname == "Twinrix" ~ "hepab",
vacname == "TRITANRIX-HB" ~ "4_in_1",
vacname %in% c("Quinvaxem", "SII") ~ "5_in_1",
vacname %in% c("Hexaxim", "Hexavac", "Infanrix Hexa") ~ "6_in_1",
.default = NULL # default case
))data <- data %>% mutate(vacname2 = case_when(
vacname %in% c("ENGERIX-B", "Euvax B", "Gene-Hbvax", "Heberbiovac HB", "Hep B vaccine", "Hepavax-Gene TF") & vac_age < 30 ~ "possible_hep_b_vaccine_for_newborn",
.default = vacname))
data %>% filter(vacname2 == "possible_hep_b_vaccine_for_newborn") %>% nrow()## [1] 434868data %>% filter(vacname2 == "Hep B vaccine for newborn") %>% nrow()## [1] 7737858Create a province vaccination coverage function based on the number of births for each province in Vietnam, using data from GSO: https://www.gso.gov.vn/dan-so/. Download the data and save it as ‘province.xlsx’.
province_coverage_plot <- function(data_temp) {
province <- read_xlsx(here::here("data/province.xlsx"))
frequency_table <- table(data_temp$province_reg, data_temp$year_dob)
frequency_table <- as.data.frame(frequency_table)
frequency_table <- frequency_table %>% rename(province_reg = Var1 , year_dob = Var2)
province <- left_join(x = frequency_table, y = province, by = c("province_reg","year_dob" ))
province <- province %>% mutate(coverage = (Freq/birth_number)*100)
province <- province %>% mutate(coverage = format(round(coverage, 1), nsmall = 1, scientific = FALSE))
province_wide <- province %>% select(province_reg, year_dob, coverage) %>% pivot_wider(id_cols = province_reg, names_from = year_dob, values_from = coverage)
gg <- ggplot(province, aes(x = factor(year_dob), y = as.numeric(coverage), group = factor(province_reg), color = factor(province_reg))) +
geom_line() +
labs(title = "Coverage by Year",
x = "Year",
y = "Coverage") +
facet_wrap(~province_reg) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 5)) +
scale_y_continuous(breaks = seq(0, max(province$coverage), by = 20))
# Convert ggplot to an interactive plot using plotly
ggplotly(gg)
}The birth numbers for each region are based on GSO data. The birth numbers for Binh Dinh have been deducted from Region 3, as our vaccination data does not include Binh Dinh province.For GSO data, visit the official website
region_coverage_plot <- function(data_temp) {
region <- read_xlsx(here::here("data/region.xlsx"))
frequency_table <- table(data_temp$region, data_temp$year_dob)
frequency_table <- as.data.frame(frequency_table)
frequency_table <- frequency_table %>% rename(region = Var1, year_dob = Var2)
region <- left_join(x = frequency_table, y = region, by = c("region", "year_dob"))
region <- region %>% mutate(coverage = (Freq / birth_number) * 100)
region <- region %>% mutate(coverage = format(round(coverage, 1), nsmall = 1, scientific = FALSE))
region_wide <- region %>% select(region, year_dob, coverage) %>% pivot_wider(id_cols = region, names_from = year_dob, values_from = coverage)
gg <- ggplot(region, aes(x = factor(year_dob), y = as.numeric(coverage), group = factor(region), color = factor(region))) +
geom_line() + # Use geom_line instead of geom_col for a line graph
labs(title = "Coverage by Year",
x = "Year",
y = "Coverage") +
facet_wrap(~region) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 5)) +
scale_y_continuous(breaks = seq(0, max(region$coverage), by = 20))
# Convert ggplot to interactive plot using plotly
ggplotly(gg)
}data <- data %>%
arrange(pid, vacdate) %>%
group_by(pid) %>%
mutate(vac_order = row_number())
setDT(data)
data <- data %>% mutate(vac_order = paste("V", vac_order, sep = ""))data %>% group_by(pid) %>% mutate ()## # A tibble: 40,421,349 × 20
## # Groups: pid [12,477,883]
## pid vacname vacdate vactype vacplace vacplace_type province_reg
## <chr> <chr> <date> <chr> <chr> <chr> <chr>
## 1 1010101201400… ENGERI… 2021-11-13 TCMR Phòng t… DV Hà Nội
## 2 1010101201400… Hepava… 2017-07-24 TCCD Trung t… DV Hà Nội
## 3 1010101201400… Hepava… 2018-04-21 TCCD Viện đà… DV Hà Nội
## 4 1010101201400… Hepava… 2016-12-04 TCCD Trung t… DV Hà Nội
## 5 1010101201400… ENGERI… 2017-08-13 TCCD Trung t… DV Hà Nội
## 6 1010101201400… ENGERI… 2017-09-16 TCCD Trung t… DV Hà Nội
## 7 1010101201400… ENGERI… 2020-08-23 TCMR Trung t… DV Hà Nội
## 8 1010101201400… ENGERI… 2020-10-10 TCMR Trung t… DV Hà Nội
## 9 1010101201400… ENGERI… 2014-09-18 TCCD Bệnh vi… DV Hà Nội
## 10 1010101201400… Hepava… 2018-04-28 TCCD Trung t… DV Hà Nội
## # ℹ 40,421,339 more rows
## # ℹ 13 more variables: district_reg <chr>, commune_reg <chr>, sex <chr>,
## # dob <date>, ethnic <chr>, year_dob <int>, year_vac <int>, vac_age <drtn>,
## # vac_month <dbl>, region <fct>, vacgroup <chr>, vacname2 <chr>,
## # vac_order <chr>data_wide <- data %>%
subset(select =c(pid, vac_order, vac_month, province_reg, year_dob, region)) %>%
pivot_wider(names_from = vac_order, values_from = vac_month)
data_wide <- data_wide %>%
mutate(
HBVnewborn_delay = ifelse(V1 == -2 , 0,
ifelse(V1 == -1 , 1,
ifelse(V1 == 0, 2, 3))))
data_wide <- data_wide %>%
mutate(
HBV1_delay = ifelse(V1 > 8, 2,
ifelse(V1 >2 , 1,
ifelse(V1 == 2 , 0,
ifelse(V1 == 1, -1,
ifelse(V2 > 8, 2,
ifelse(V2 > 2, 1,
ifelse(V2 == 2, 0, -1))))))))
data_wide <- data_wide %>%
mutate(
HBV2_delay = ifelse(V2 > 9, 2,
ifelse(V2 > 3 , 1,
ifelse(V2 == 3 , 0,
ifelse(V1 >= 1, -1,
ifelse(V3 > 9, 2,
ifelse( V3 > 3, 1,
ifelse(V3 == 3, 0, -1))))))))
data_wide <- data_wide %>%
mutate(
HBV3_delay = ifelse(V3 > 10, 2,
ifelse(V3 > 4 , 1,
ifelse(V3 == 4 , 0,
ifelse(V2 >= 3, -1,
ifelse( V4 > 10, 2,
ifelse(V4 > 4, 1,
ifelse(V4 == 4, 0, -1))))))))
data_wide %>% filter(HBV1_delay == 0, year_dob == 2018) %>% nrow()## [1] 695231data_wide %>% filter(HBV1_delay == 1, year_dob == 2018) %>% nrow()## [1] 745797data_wide %>% filter( HBV1_delay == 0, year_dob == 2017) %>% nrow()## [1] 1011876data_wide %>% filter( HBV1_delay == 1, year_dob == 2017) %>% nrow()## [1] 608054data_wide %>% filter( HBVnewborn_delay == 0, year_dob == 2018, province_reg == "Bạc Liêu") %>% nrow()## [1] 13313data_temp <- data_wide %>% filter(HBVnewborn_delay == 0)
interactive_plot <- region_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBVnewborn_delay %in% c(0,1))
interactive_plot <- region_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV1_delay == 0 )
interactive_plot <- region_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV2_delay == 0 )
interactive_plot <- region_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV3_delay == 0)
interactive_plot <- region_coverage_plot(data_temp)
interactive_plotIn this report, we highlight a concerning trend regarding the age-specific vaccination coverage of children born in 2019. According to a source, the coverage is low, primarily due to the impact of COVID-19. The current situation emphasizes the importance of addressing vaccination challenges, especially in the context of the ongoing pandemic.
data_temp <- data_wide %>% filter(HBVnewborn_delay == 0 )
interactive_plot <- province_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBVnewborn_delay %in% c(0,1) )
interactive_plot <- province_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV1_delay == 0)
interactive_plot <- province_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV2_delay == 0)
interactive_plot <- province_coverage_plot(data_temp)
interactive_plotdata_temp <- data_wide %>% filter(HBV3_delay == 0)
interactive_plot <- province_coverage_plot(data_temp)
interactive_plotlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBVnewborn_delay == 0, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBVnewborn_delay %in% c(0,1), region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV1_delay ==0, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV2_delay == 0, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV3_delay == 0, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV1_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV2_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV3_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV1_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV2_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKDlibrary(dplyr)
create_interactive_plot <- function(data_wide, selected_region) {
data_temp <- data_wide %>%
filter(HBV3_delay == 1, region == selected_region)
interactive_plot <- province_coverage_plot(data_temp)
return(interactive_plot)
}
# Example usage
regions_of_interest <- c("RRD", "NMM", "NCC", "CHL", "SE", "MKD")
for (region in regions_of_interest) {
plot_name <- paste0("interactive_plot_", region)
assign(plot_name, create_interactive_plot(data_wide, region))
}interactive_plot_RRDinteractive_plot_NMMinteractive_plot_NCCinteractive_plot_CHLinteractive_plot_SEinteractive_plot_MKD